Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network

06/04/2022
by   Endai Huang, et al.
0

Computer vision enables the development of new approaches to monitor the behavior, health, and welfare of animals. Instance segmentation is a high-precision method in computer vision for detecting individual animals of interest. This method can be used for in-depth analysis of animals, such as examining their subtle interactive behaviors, from videos and images. However, existing deep-learning-based instance segmentation methods have been mostly developed based on public datasets, which largely omit heavy occlusion problems; therefore, these methods have limitations in real-world applications involving object occlusions, such as farrowing pen systems used on pig farms in which the farrowing crates often impede the sow and piglets. In this paper, we propose a novel occlusion-resistant Center Clustering Network for instance segmentation, dubbed as CClusnet-Inseg. Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M) and Remain-Centers-to-Mask (RC2M) algorithms, and a pseudo-occlusion generator (POG). In all, 4,600 images were extracted from six videos collected from six farrowing pens to train and validate our method. CClusnet-Inseg achieves a mean average precision (mAP) of 83.6; it outperformed YOLACT++ and Mask R-CNN, which had mAP values of 81.2 and 74.7, respectively. We conduct comprehensive ablation studies to demonstrate the advantages and effectiveness of core modules of our method. In addition, we apply CClusnet-Inseg to multi-object tracking for animal monitoring, and the predicted object center that is a conjunct output could serve as an occlusion-resistant representation of the location of an object.

READ FULL TEXT

page 4

page 5

page 6

page 10

page 11

page 12

research
07/24/2018

ClusterNet: Instance Segmentation in RGB-D Images

We propose a method for instance-level segmentation that uses RGB-D data...
research
08/12/2019

Explicit Shape Encoding for Real-Time Instance Segmentation

In this paper, we propose a novel top-down instance segmentation framewo...
research
12/29/2020

FPCC-Net: Fast Point Cloud Clustering for Instance Segmentation

Instance segmentation is an important pre-processing task in numerous re...
research
11/24/2017

Distance to Center of Mass Encoding for Instance Segmentation

The instance segmentation can be considered an extension of the object d...
research
04/29/2022

Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being

Chicken well-being is important for ensuring food security and better nu...
research
12/02/2020

Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation

Blastomere instance segmentation is important for analyzing embryos' abn...
research
07/09/2019

Accurate Nuclear Segmentation with Center Vector Encoding

Nuclear segmentation is important and frequently demanded for pathology ...

Please sign up or login with your details

Forgot password? Click here to reset